摘要
掘进机是煤矿井下机械化掘进巷道的主要工程设备,其工作可靠性、稳定性、故障率等直接影响巷道进尺量、断面成形及截割效率。针对掘进机故障频繁发生而且难于诊断的现状,提出了利用主成分分析方法对掘进机实时数据进行特征信息提取,建立掘进机故障变量的诊断模型。结果表明,该方法在信息量损失较小的前提下,可以对掘进机故障作出定量评价。
The road-header is the main equipment of coal mine mechanization tunneling, its reliability,stability, failure rate directly affect mining distance,section forming and cutting efficiency. Aiming at roadheader fault occurred frequently and difficult to diagnose present situation, proposed the using the principal component analysis method to real-time data characteristic information road-header extraction, establish road-header fault variables diagnosis model. The results show that the smaller loss in the amount of information under the premise of the road-header can make quantitative evaluation of the fault.
出处
《煤矿机械》
北大核心
2014年第8期276-278,共3页
Coal Mine Machinery
关键词
掘进机
故障诊断
主成分分析
过程监测
roadheader
fault diagnosis
principal component analysis
process monitoring
